Real-time estimation of fluid compositions and properties using downhole optical tools is challenging for well testing and sampling in the oil and gas industry. To make real-time fluid analysis, current practice often pre-selects a set of predictive models calibrated in a synthetic database as operational fluid models. However, without using a ruggedized validation method, fluid model pre-selection based on a synthetic calibration database alone may be problematic and curtailed by the limitation of existing databases, especially when a new tool is deployed for the first time in the field. In such cases, problems such as lack of information of signal variation in the real tool system during testing and sampling may occur, and there may be difficulty in using available field data and results in assisting decision making.
Current practice in calibrating fluid predictive models is also sensor dependent. Therefore, field data and results obtained from a particular tool may not be able to validate model selection of other tools in which different optical sensors are used. The issue of data management with individual-sensor-based fluid model calibration may also arise with changes in optical sensor design and updates of calibration databases. Future technology development applied to optical fluid analysis would be hard to implement without data sharing and integration among the tools.
In the figures, elements having the same or similar reference numerals refer to the same or similar function, or step, unless otherwise noted.